The development of new products is attracting increasing attention from industry and academia due, among other factors, to the reduced product development process, market differentiation, greater product complexity, rapid changes in technological knowledge and increased customer sophistication. This scenario requires an efficient cost estimation process and quick decision-making to ensure the efficiency of the production process. Consequently, many approaches have been suggested for use in predicting product costs. However, each has its issues and limitations that affect the effectiveness of the final solution. This paper aims to survey approaches related to the cost estimation process in aerospace industry processes and their respective niches, highlighting the gaps and limitations present to identify the appropriate contexts for applying each. It compiles the latest work on cost estimation approaches, artificial neural networks, and the semantic web in the aerospace industry, using the knowledge acquired for better and comprehensive future implementation.